Discovering new question and answer knowledge from conversation

ABSTRACT

New question and answer (QA) pairs can be automatically discovered from a corpus of data such as online chats and conversations. Newly discovered QA pairs can augment QA database, which can be used by a computer processor or device, e.g., by a chatbot, an automated machine, and/or another. Existing QA knowledge can be used to learn the structures of QA knowledge distribution in conversations, and new QA knowledge can be automatically learned through the structure of learned QA knowledge distribution in conversations. The structure of learned QA knowledge distribution can be refined by adding more semantics based on labeled data.

BACKGROUND

The present application relates generally to computers and computerapplications, and more particularly to natural language processing anddiscovering new question and answer knowledge from conversations, andautomated processor or robot that can carry on a conversation with auser.

A computer, e.g., such as one running a chatbot can conduct an on-linechat conversation, for example, by text or speech, with a user. Such acomputer and/or chatbot may access or learn from existing conversations,for example, dialogs including questions and answers, to continue totune and test, to improve itself. For instance, a chatbot that providescustomer care experiences may need to recognize and follow variousgoal-driven patterns in conversations in servicing a customer.

BRIEF SUMMARY

The summary of the disclosure is given to aid understanding of acomputer system and method of discovering new question and answerknowledge from conversations, and not with an intent to limit thedisclosure or the invention. It should be understood that variousaspects and features of the disclosure may advantageously be usedseparately in some instances, or in combination with other aspects andfeatures of the disclosure in other instances. Accordingly, variationsand modifications may be made to the computer system and/or their methodof operation to achieve different effects.

A system of discovering new question and answer knowledge fromconversation, in an aspect, can include a processor and a memory devicecoupled with the processor. The processor can be configured to receive aquestion and answer pair including a question and a correspondinganswer. The processor can also be configured to search a corpus ofconversations to find first conversation segments containing thequestion and answer pair, the conversation segments containingstatements including the question, the answer and intermediatestatements in-between the question and the corresponding answer. Theprocessor can also be configured to tag the statements in the firstconversation segments with dialog labels. For example, the firstconversation segments can be transformed into sequences of dialoglabels, a sequence of dialog labels representing a question and answerstructure pattern of a first conversation segment, where question andanswer structure patterns are formed respectively corresponding to thefirst conversation segments. The processor can also be configured tosearch the corpus of conversations to find second conversation segmentshaving at least one of the question and answer structure patterns. Foreach of the question and answer structure patterns, the processor can beconfigured to receive labels associated with the second conversationsegments. The processor can also be configured to compute effectivenessof each of the question and answer structure patterns based on thereceived labels. The processor can also be configured to select aquestion and answer structure pattern meeting an effectivenessthreshold. The processor can also be configured to transform theselected question and answer structure pattern into a new question andanswer.

A computer-implemented method, in an aspect, can include receiving aquestion and answer pair including a question and a correspondinganswer. The method can also include searching a corpus of conversationsto find first conversation segments containing the question and answerpair. The first conversation segments can contain statements includingthe question and the answer and intermediate statements in-between thequestion and the corresponding answer. The method can also includetagging the statements in the first conversation segments with dialoglabels. For example, the first conversation segments can be transformedinto sequences of dialog labels. A sequence of dialog labels canrepresent a question and answer structure pattern of a firstconversation segment, where question and answer structure patterns areformed respectively corresponding to the first conversation segments.The method can also include searching the corpus of conversations tofind second conversation segments having at least one of the questionand answer structure patterns. The method can also include, for each ofthe question and answer structure patterns, receiving labels associatedwith the second conversation segments. The method can also include,based on the received labels, computing effectiveness of each of thequestion and answer structure patterns. The method can also includeselecting a question and answer structure pattern meeting aneffectiveness threshold. The method can also include transforming theselected question and answer structure pattern into a new question andanswer.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram showing domain-based QA pair pattern discoveryin an embodiment, for example, discovering a question and answer pair.

FIG. 2 is a flow diagram illustrating automatic refinement of dialogpattern in an embodiment.

FIG. 3 is a flow diagram illustrating a method in an embodiment ofdiscovering question and answer pair in conversations.

FIG. 4 is a diagram showing components of a system in one embodimentthat discovers new question and answer (QA) knowledge from one or moreconversations.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment.

FIG. 6 illustrates a cloud computing environment in one embodiment.

FIG. 7 illustrates a set of functional abstraction layers provided bycloud computing environment in one embodiment of the present disclosure.

DETAILED DESCRIPTION

Systems and methods can be provided, which can automatically identify,decompose, model and automate question and answer (QA) patterns andprovide for fine-grained learning and orchestration at runtime. By wayof example, customer care experience is one example area, whereconversations are often composed of various goal-driven flow and add-onflow patterns. In one or more embodiments, systems and methods candiscover QA pair structure patterns, which can support subject matterexpert (SME) building QA knowledge from on-going large-scaleconversations. The systems and methods in one or more embodiments canleverage existing QA knowledge to learn the typical structures of QAknowledge distribution in conversations, automatically discover new QAknowledge through the structure of learned QA knowledge distribution inconversations, and refine the structure of learned QA knowledgedistribution by adding more semantics based on labeled data.

FIG. 1 is a flow diagram showing domain-based QA pair pattern discoveryin an embodiment, for example, discovering a question and answer pair.The component shown can be implement and/or run on one or more computerprocessors, e.g., hardware processors. One or more hardware processors,for example, may include components such as programmable logic devices,microcontrollers, memory devices, and/or other hardware components,which may be configured to perform respective tasks described in thepresent disclosure. Coupled memory devices may be configured toselectively store instructions executable by one or more hardwareprocessors.

A processor may be a central processing unit (CPU), a graphicsprocessing unit (GPU), a field programmable gate array (FPGA), anapplication specific integrated circuit (ASIC), another suitableprocessing component or device, or one or more combinations thereof. Theprocessor may be coupled with a memory device. The memory device mayinclude random access memory (RAM), read-only memory (ROM) or anothermemory device, and may store data and/or processor instructions forimplementing various functionalities associated with the methods and/orsystems described herein. The processor may execute computerinstructions stored in the memory or received from another computerdevice or medium.

In an embodiment, a conversation segment starts from an identifiedquestion utterance and ends at an answer utterance, include all dialogutterances between the question utterance and answer utterance. In anembodiment, an existing corpus of conversations or dialogs, for exampleswitch board corpus or dialog corpus or data including dialogs, forexample, question and answer type of conversations, which have beentagged can be used. For instance, existing databases may include labeleddialogs or utterances (e.g., referred to as dialog labels). Forinstance, Dialog Act Markup in Several Layers (DAMSL) is an examplemethodology, which includes annotated dialogs. By way of example, anexisting dialog labeling methodology uses the following labels thatannotate statements or utterances in a dialog to indicate the types ofstatements: statement-non-opinion (e.g., “me, I'm in the legaldepartment”), acknowledge (backchannel) (e.g., “Uh-huh”),statement-opinion (e.g., “I think it's great”), agree/accept (e.g.,“That's exactly it”), abandoned or turn-exit (e.g., “So, --”),appreciation (e.g., “I can imagine”), yes-no-question (e.g., “Do youhave any special training?”), non-verbal (e.g., [Laughter],[Throat_clearing]), yes answer (e.g., “Yes”), conventional-closing(e.g., “Well, it's been nice talking to you”), uninterpretable (e.g.,“But, uh, yeah”, wh-question (e.g., “Well, how old is it?”), no answers(e.g., “No”), response acknowledgment (e.g., “Oh, okay”), hedge (e.g.,“I don't know if I'm making any sense or not”), declarativeyes-no-question (e.g., “So you can afford to get it?”), other (e.g.,“Well, give me a break, you know”), backchannel in question form (e.g.,“Is that right?”), quotation.

An example of a conversation or dialog can include a conversation orchat between a customer and an automated online customer service (e.g.,a chatbot) for providing a service to the customer. For instance, acustomer can type in or utter a question, and a chatbot canautomatically answer the question or ask further questions, carrying ona dialog with the customer, in order to provide the answer. The chatbot,for example, can use natural language processing and machine learningtechniques for conversing with the user. In an embodiment, a questionand answer dialog can be discovered and saved for future use, forinstance, in servicing a customer online. For instance, an automatedprocessor or a robot may provide answers to a customer's questions onbased on the discovered similar dialog.

Referring to FIG. 1 , at 102, there can exist a corpus of pre-defined(e.g., manually defined) QA pairs, which can be used. For instance, oneor more pre-defined QA pairs can be received.

At 104, content search is performed. For example, a processor can searcha corpus or database of conversations or chats, to find a conversationthat contains a pre-defined QA pair, for example, received at 102. Thismay be done for each of the pre-defined QA pairs in the corpus. Forexample, a conversation that contains a question and answer segment thatis like the pre-defined QA pair can be identified. One or more naturallanguage processing techniques can be used to find such conversations.

At 122, optionally, conversations that do not actually contain the QApairs can be excluded, for example, manually. For instance,conversations segments or snippets identified or labeled as containing aQA pair may not actually have a question statement or answer statement.Such segments can be verified manually (e.g., by a user) and omitted.

At 106, a processor may perform labeling and semantic analysis ofconversation segments in the conversation. For instance, sentencesappearing between the question and answer like the QA pair, in theconversation can be labeled. For example, for each sentence, there canbe one label such as a dialog act label or tag. A conversation segmentcontaining the question and the answer, and one or more statementsoccurring in between the question and the answer can be labeled, forminga sequence of labels. For instance, a conversation segment istransformed into a sequence of labels, for example, dialog acts. Asequence of labels forms a domain-based QA pair structure pattern. Therecan be a plurality of such domain-base QA pair structure patterns, forexample, since there can be different conversation segments whichinclude the question and the answer. In an aspect, the labeling andanalysis are domain-based, for example, pertains to a particular domain.

At 108, a processor may perform a domain-based QA pair structure patterndiscovery. For example, a conversation segment containing a sequence oflabels (e.g., a sequence of dialog acts) corresponding to the questionand the answer, and in-between statements can be discovered. A sequenceof labels for a conversation segment is also referred to as a QA pairstructure pattern. There can be multiple different QA pair structurepatterns.

At 110, a processor may search a conversation database or corpuscontaining the QA pair structure patterns, e.g., sequences of labels.For instance, the existing conversations are searched based on labelpatterns rather than their literary content.

At 112, a processor may sample the identified conversation segments thatcontain the sequence of labels to reduce the number of the identifiedconversation segments to a smaller set of conversation segments. In anembodiment, sampling can be performed by randomly selecting a subset ofconversations that contain QA pair structure patterns, from the originalwhole set of conversations found at 110.

At 114, each sample conversation can be labeled. In an embodiment, thislabeling can be done manually. For instance, a user or an agent maymanually determine whether the sampled conversation contains a questionand a corresponding answer. For example, given a conversation segment, auser or an agent may label whether that conversation segment contains aquestion and a corresponding answer, for example, in the form of atleast one of the QA pair structure patterns. In an embodiment, manuallabeling can be performed based on human judgment on whether a questionand a corresponding answer appear in the sample conversation. Whenlabeling, the user may find a question statement, and after that, lookfor a corresponding answer statement.

At 116, based on the labeled data, a processor may conduct self-learningon the QA pair structure patterns. This self-learning optimizes theresult of pattern discovery and can rank the patterns. For example,based on the labeled data, the processor can compute effectiveness(e.g., an effectiveness score) associated with each of the QA pairstructure patterns. This self-learning is described further withreference to FIG. 2 .

At 118, a processor may select one or more QA pair structure patternshaving a confidence score that is higher than a predefined thresholdscore. The predefined threshold score can be configurable.

At 120, for example, at run time of an online QA system, a processor mayselect the chosen QA pair structure pattern to generate a new QA pair.For example, a method disclosed herein can be enabled for an onlineconversation system, e.g., during a conversation conducted between auser and a customer support or service, and/or a chat application or thelike running on a computer or a hardware processor. In an embodiment, aconversation carried on thusly can be checked to determine whether theconversation flow satisfies one or more of the identified QA pairstructure patterns, and if yes, a QA pair identified in the conversationcan be extracted and stored. For instance, question and answer from aconversation segment containing the chosen QA pair structure pattern,can be extracted. Such question and answer can be different from thequestion and answer received at 102, for instance, since the discoveryis performed based on the QA pair structure pattern and not the literalcontent of the question. For instance, the generated new QA pair can beabout a different subject or topic in the domain from the question andanswer received at 102. The new QA pair can be stored, for example, forautomatically conducting an online conversation with a user.

In an embodiment, the methodology disclosed herein can provide apowerful tool for the subject matter expert (SME) to handle large volumeof data, for example, for QA discovery. Such a tool can be used togenerated more effective and accurate online automated conversationswhich a machine such as a computer or an autobot (e.g., a robot) cancarry on with a user.

FIG. 2 is a flow diagram illustrating automatic refinement of dialogpattern in an embodiment. The processor or flow shown in FIG. 2 can berun on or implemented by one or more computer processors, for example,including one or more hardware processors. At 202, a sequence of dialoglabels or acts are discovered, e.g., a structural pattern of aconversation segment is discovered based on the labels of the statementsin the conversation segment. For example, a frequent pattern of dialogacts or labels (based on a frequency threshold) can be discovered. Forinstance, one or more patterns that occur most frequently inconversations or based on a frequency threshold can be selected oridentified.

At 204, pattern discovery effectiveness on the labeled data can bechecked. A number of conversation segments can be labeled to indicatewhether those conversation segments contain the structural pattern. Forexample, a user labeled conversation segments can be received. Forinstance, consider an example pattern P1 containing dialog labels oracts <a1, a2, a3, a4>, which occurs in conversations. Also consideranother example pattern P2 containing dialog labels or acts <a3, a5, a6,a7>. Consider the following labeled conversations as applied to P1pattern: C1(Y), C2(Y), C3(N), C4(Y). Out of the 4 conversation segmentsC1, C2, C3, and C4, a user labeled C1, C2 and C4 with “Y”, indicatingthat those conversation segments contain the example pattern P1 with aquestion and a corresponding answer. In this example case, 3 out of 4conversation segments are correctly identified as containing thequestion and answer, and hence the effectiveness or score is ¾ or 0.75.Similarly, consider the following labeled conversations as applied to P2pattern: C5(N), C6(N), C8(Y), C9(Y). In this example case, 2 out of 4conversation segments are correctly identified as containing thequestion and answer, and hence the effectiveness or score associatedwith P2 pattern is 2/4 or 0.5.

If the threshold score is set to 0.7, then only P1 is selected. Suchconversation pattern can be used in a real time online conversation inconversing with a user. The method at 204 also illustrates theself-learning and choosing high confidence QA pair structure pattern orpatterns shown in FIG. 1 at 116 and 118.

For example, at 206, it is determined whether the pattern discoveryeffectiveness is greater than the threshold. If yes, then the pattern isgenerated as a candidate at 210. For example, consider that thethreshold is 0.7. Considering the above P1 pattern as an example, andthat the effectiveness of P1 pattern is computed to be 0.75, the P1pattern is generated as a candidate pattern, for example, and saved forfuture use.

If the pattern discovery effectiveness is not greater than thethreshold, further refining of the pattern can be performed. Forinstance, considering the above P2 pattern as an example, P2 can befurther refined into P2′ pattern. At 208, a dialog label or act can berandomly selected and a corresponding semantics can be added. Forexample, in the example pattern P2 containing dialog labels or acts <a3,a5, a6, a7>, a label can be selected randomly, e.g., <a5>, and using anatural language technique, semantics such as intent associated with thestatement corresponding to <a5> label in a conversation (e.g., any oneor more original conversation where the pattern was discovered), can beextracted, and added to the pattern. So for example, P2 is transformedor refined to P2′, which includes <a3, <a5, intent1>, a6, a7>. In thisexample, only conversations having the pattern of <a3, <a5, intent1>,a6, a7> would be identified, e.g., C5(N), C8(Y), C9(Y). In this example,using the refined P2′ pattern eliminated C6(N) from the above exampleconversations segments of P2 pattern: C5(N), C6(N), C8(Y), C9(Y),because C6 segment did not contain <a5, intent1>. In this example, at204, the effectiveness of P2′ is computed to be ⅔ (e.g., 2 out of 3conversations have been correctly identified as relating to the questionand answer pair). At 206, since ⅔ is still not greater than thethreshold score in this example (e.g., 0.7), the processing can repeatagain at 208.

For instance, P2′ can be further transformed to P2″ to include thesemantics of another randomly selected dialog label, e.g., <a6> tocontain its intent, <a6, intent2>. So for example, the transformed P2″can be <a3, <a5, intent1>, <a6, intent2>, a7>. Searching in C5(N),C8(Y), C9(Y) for the pattern P2″ may further eliminate additionalincorrectly identified conversation segment, e.g., C5(N), for instance,if C5(N) does not include <a6, intent2>. So in this example, patterndiscovery effectiveness associated with P2″ is computed to be 2/2, e.g.,C8(Y), C9(Y). Since 2/2 is larger than 0.7, P2″ can be generated as apattern candidate at 210.

In an embodiment, <a3, <a5, intent1>, <a6, intent2>, a7> can be thenstored or saved in a database of QA pairs and associated structures forfuture use.

FIG. 3 is a flow diagram illustrating a method in an embodiment. Themethod can be performed or implemented on one or more computerprocessors, for example, including one or more hardware processors. At302, a predefined question and answer can be received. Such a questionand answer pair includes a question and a corresponding answer. Aquestion and corresponding answer are also referred to as a question andanswer (QA) pair. Such predefined question and answer pair can beretrieved from a corpus of existing QA pairs.

At 304, a corpus of conversations (e.g., a database of online chats,conversations and/or the like is searched to find first conversationsegments containing the question and answer pair. The conversationsegments contain statements or utterances including the question and theanswer and intermediate statements in-between the question and thecorresponding answer. For instance, a conversation can contain aquestion such as “Would length L fit size S?” and a corresponding answersuch as “Size S is too small for length L.” In between such question andanswer in the conversation, e.g., after the question and before theanswer is given, there can be one or more intermediate statements orutterances, which provide addition information for leading to theanswer, e.g., “Is the width standard?”, “no width is W”, and/or others.

At 306, the statements in the first conversation segments are tagged orlabeled with dialog labels. By tagging or labeling the statements, thefirst conversation segments can be transformed into sequences of dialoglabels. For instance, a sequence of dialog labels represents a questionand answer structure pattern of a first conversation segment. Questionand answer (QA) structure patterns are formed respectively correspondingto the first conversation segments, for example, one QA structurepattern per one conversation segment.

At 308, the corpus of conversations is searched to find secondconversation segments having at least one of the question and answerstructure patterns. In an embodiment, the second conversation segmentsfound in the search can be further sampled to reduce the number of thesecond conversation segments for processing.

At 310, for each of the question and answer structure patterns, labelsor annotations associated with the second conversation segments arereceived. These labels or annotations indicate which one of the secondconversation segments include a question and an answer sequence ofstatements that follow a question and answer structure patterns. In anembodiment, a conversation segment (e.g., a second conversation segment)has a corresponding label or annotation indicating whether thatconversation segment follows a given question and answer structurepattern. The conversation segments are labeled or annotated based oneach of the question and answer structure patterns. So, for example, ifthere are 5 different question and answer structure patterns, aconversation segment would be labeled 5 times, e.g., would have 5 labelsor annotations. In an embodiment, the received labels or annotations aremanually labeled labels or annotations, e.g., annotated by a user.

At 312, based on the received labels, effectiveness of each of thequestion and answer structure patterns is computed. For instance, for aquestion and answer structure pattern, an effectiveness score can becomputed using the labels of the second conversation segments pertainingto that question and answer structure pattern. An example computation ofsuch a score is described with reference to FIG. 2 at 204.

At 314, a question and answer structure pattern meeting an effectivenessthreshold is selected. The effectiveness threshold can be configurable,and/or can be predefined.

At 316, the selected question and answer structure pattern istransformed into a new question and answer pair. For example, aconversation segment (second conversation segment) having the selectedquestion and answer structure pattern is identified, and the questionand answer in that identified conversation segment is extracted as a newquestion and answer pair.

In an embodiment, at least one of the question and answer structurepatterns can be refined. For instance, a first conversation segmentand/or a second conversation segment having a question and answerstructure pattern can be further analyzed to determine an intentassociated with one or more of the statements in the conversationsegments. A natural language technique can be used to determine suchintent or semantics associated with a statement or utterance. Thedetermined intent can be added as associated with a dialog labelcorresponding to the statement having that intent, the dialog labelwhich is contained in the question and answer structure pattern beingrefined. Example of such refinement is described with reference to FIG.2 . In an embodiment, a dialog label in the question and answerstructure pattern being refined can be randomly selected as a candidatefor adding intent to that dialog label (e.g., statement represented bythat dialog label). In an embodiment, a question and answer structurepattern that does not meet the effectiveness threshold (e.g., at 314),or below the effectiveness threshold, can be selected for furtherrefinement. In an embodiment, applying the refined question and answerstructure pattern to one or more of the conversation segments caneliminate those that does not contain the refined question and answerstructure pattern. For instance, a conversation segment previouslyidentified as having the question and answer structure pattern may notcontain the refined version of the question and answer structurepattern, and thus that conversation segment can be eliminated orexcluded from consideration.

FIG. 4 is a diagram showing components of a system in one embodimentthat discovers new question and answer (QA) knowledge from one or moreconversations. One or more hardware processors 402 such as a centralprocessing unit (CPU), a graphic process unit (GPU), and/or a FieldProgrammable Gate Array (FPGA), an application specific integratedcircuit (ASIC), and/or another processor, may be coupled with a memorydevice 404, and generate a prediction model and recommend communicationopportunities. A memory device 404 may include random access memory(RAM), read-only memory (ROM) or another memory device, and may storedata and/or processor instructions for implementing variousfunctionalities associated with the methods and/or systems describedherein. One or more processors 402 may execute computer instructionsstored in memory 404 or received from another computer device or medium.A memory device 404 may, for example, store instructions and/or data forfunctioning of one or more hardware processors 402, and may include anoperating system and other program of instructions and/or data.

One or more hardware processors 402 may receive input including aquestion and answer (QA) pair. For example, a database or corpus of dataincluding QA pairs, which have been predefined can be received, and usedto discover a new QA pair. One or more processors may also receive oraccess a corpus of conversations such as online chats or other form ofconversations. At least one hardware processor 402 may generate a new QApair, for example, by using QA pair structure patterns identified inconversations. Data such as conversation data, predefined QA pairs,annotated conversation data may be stored in a storage device 406 orreceived via a network interface 408 from a remote device, and may betemporarily loaded into a memory device 404 for generating one or moreQA structure patterns and new QA pair. One or more hardware processors402 may be coupled with interface devices such as a network interface408 for communicating with remote systems, for example, via a network,and an input/output interface 410 for communicating with input and/oroutput devices such as a keyboard, mouse, display, and/or others.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment. The computersystem is only one example of a suitable processing system and is notintended to suggest any limitation as to the scope of use orfunctionality of embodiments of the methodology described herein. Theprocessing system shown may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the processingsystem shown in FIG. 5 may include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,handheld or laptop devices, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems or devices,and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being run by acomputer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 that performsthe methods described herein. The module 30 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

It is understood in advance that although this disclosure may include adescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed. Cloud computing is a model of service delivery forenabling convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g. networks, network bandwidth,servers, processing, memory, storage, applications, virtual machines,and services) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service. Thiscloud model may include at least five characteristics, at least threeservice models, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 6 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 6 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 7 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and QA discovery processing 96.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, run concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be run in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts or carry outcombinations of special purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. As used herein, the term “or” is an inclusive operator andcan mean “and/or”, unless the context explicitly or clearly indicatesotherwise. It will be further understood that the terms “comprise”,“comprises”, “comprising”, “include”, “includes”, “including”, and/or“having,” when used herein, can specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. As used herein, the phrase “in an embodiment” does notnecessarily refer to the same embodiment, although it may. As usedherein, the phrase “in one embodiment” does not necessarily refer to thesame embodiment, although it may. As used herein, the phrase “in anotherembodiment” does not necessarily refer to a different embodiment,although it may. Further, embodiments and/or components of embodimentscan be freely combined with each other unless they are mutuallyexclusive.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A system comprising: a processor; and a memorydevice coupled with the processor; the processor configured to: receivea question and answer pair including a question and a correspondinganswer; search a corpus of conversations to find first conversationsegments containing the question and answer pair, the first conversationsegments containing statements including the question and thecorresponding answer, and intermediate statements in-between thequestion and the corresponding answer; tag the statements in the firstconversation segments with dialog labels, the first conversationsegments transformed into sequences of dialog labels, a sequence ofdialog labels representing a question and answer structure pattern of afirst conversation segment, wherein question and answer structurepatterns are formed respectively corresponding to the first conversationsegments; search the corpus of conversations to find second conversationsegments having at least one of the question and answer structurepatterns; for each of the question and answer structure patterns,receive labels associated with the second conversation segments; basedon the received labels, compute effectiveness of each of the questionand answer structure patterns; select a question and answer structurepattern meeting an effectiveness threshold; and transform the selectedquestion and answer structure pattern into a new question and answer. 2.The system of claim 1, wherein the processor is further configured tosample the second conversation segments found in the search to reducethe number of the second conversation segments for processing.
 3. Thesystem of claim 1, wherein the received labels are manually labeledlabels, indicating which of the second conversation segments includestatements that follow the question and answer structure patterns. 4.The system of claim 1, wherein the processor is further configured torefine at least one of the question and answer structure patterns byfurther analyzing at least one of the first conversation segment and thesecond conversation segment having said at least one of the question andanswer structure patterns, and adding an intent associated with a dialoglabel contained in said at least one of the question and answerstructure patterns.
 5. The system of claim 4, wherein the dialog labelis randomly selected for adding the intent.
 6. The system of claim 4,wherein the processor is configured to refine said at least one of thequestion and answer structure patterns, which is below the effectivenessthreshold.
 7. The system of claim 6, wherein the refined question andanswer structure pattern eliminates at least one labeled conversationsegment having said at least one of the question and answer structurepatterns.
 8. The system of claim 1, wherein the received question andanswer pair includes a predefined question and answer pair retrievedfrom a database storing manually defined question and answer pairs.
 9. Acomputer-implemented method comprising: receiving a question and answerpair including a question and a corresponding answer; searching a corpusof conversations to find first conversation segments containing thequestion and answer pair, the first conversation segments containingstatements including the question and the corresponding answer andintermediate statements in-between the question and the correspondinganswer; tagging the statements in the first conversation segments withdialog labels, the first conversation segments transformed intosequences of dialog labels, a sequence of dialog labels representing aquestion and answer structure pattern of a first conversation segment,wherein question and answer structure patterns are formed respectivelycorresponding to the first conversation segments; searching the corpusof conversations to find second conversation segments having at leastone of the question and answer structure patterns; for each of thequestion and answer structure patterns, receiving labels associated withthe second conversation segments; based on the received labels,computing effectiveness of each of the question and answer structurepatterns; selecting a question and answer structure pattern meeting aneffectiveness threshold; and transforming the selected question andanswer structure pattern into a new question and answer.
 10. The methodof claim 9, further including sampling the second conversation segmentsfound in the search to reduce the number of the second conversationsegments for processing.
 11. The method of claim 9, wherein the receivedlabels are manually labeled labels, indicating which of the secondconversation segments include statements that follow the question andanswer structure patterns.
 12. The method of claim 9, further includingrefining at least one of the question and answer structure patterns byfurther analyzing at least one of the first conversation segment and thesecond conversation segment having said at least one of the question andanswer structure patterns, and adding an intent associated with a dialoglabel contained in said at least one of the question and answerstructure patterns.
 13. The method of claim 12, wherein the dialog labelis randomly selected for adding the intent.
 14. The method of claim 12,wherein the refining includes refining said at least one of the questionand answer structure patterns, which is below the effectivenessthreshold.
 15. The method of claim 14, wherein the refined question andanswer structure pattern eliminates at least one labeled conversationsegment having said at least one of the question and answer structurepatterns.
 16. A computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions readable by a device to cause the device to:receive a question and answer pair including a question and acorresponding answer; search a corpus of conversations to find firstconversation segments containing the question and answer pair, theconversation segments containing statements including the question andthe answer and intermediate statements in-between the question and thecorresponding answer; tag the statements in the first conversationsegments with dialog labels, the first conversation segments transformedinto sequences of dialog labels, a sequence of dialog labelsrepresenting a question and answer structure pattern of a firstconversation segment, wherein question and answer structure patterns areformed respectively corresponding to the first conversation segments;search the corpus of conversations to find second conversation segmentshaving at least one of the question and answer structure patterns; foreach of the question and answer structure patterns, receive labelsassociated with the second conversation segments; based on the receivedlabels, compute effectiveness of each of the question and answerstructure patterns; select a one question and answer structure patternmeeting an effectiveness threshold; and transform the selected questionand answer structure pattern into a new question and answer.
 17. Thecomputer program product of claim 16, wherein the processor is furtherconfigured to sample the second conversation segments found in thesearch to reduce the number of the second conversation segments forprocessing.
 18. The computer program product of claim 16, wherein thereceived labels are manually labeled labels, indicating which of thesecond conversation segments include statements that follow the questionand answer structure patterns.
 19. The computer program product of claim16, wherein the processor is further configured to refine at least oneof the question and answer structure patterns by further analyzing atleast one of the first conversation segment and the second conversationsegment having said at least one of the question and answer structurepatterns, and adding an intent associated with a dialog label containedin said at least one of the question and answer structure patterns. 20.The computer program product of claim 19, wherein the dialog label israndomly selected for adding the intent.